Semi-supervised learning is an effective way to enhance model performance by mining useful information from redundant unlabeled data [1]}.
There are a few attempts to incorporate semi-supervised learning techniques into fair machine learning models [2]}.
For example, Noroozi et al. noroozi2019leveraging proposed to use the model to annotate pseudo labels for unlabeled data, and then add samples with high confidence to the training set.
They applied fairness regularization losses to both real and pseudo labeled samples.
Zhang et al. zhang2020fairness proposed an iterative method that first combines the raw dataset and the pseudo labeled dataset, then re-samples the data to ensure that the number of samples with different sensitive attributes are equal, and finally trains the model on the re-sampled dataset to further generate the pseudo labeled dataset.
However, these methods mainly aim to handle the scarcity of labeled data in downstream tasks, while they assume that the sensitive attributes of all samples are observed.
It is still very challenging to learn fair models if only a small subset of samples are associated with observed sensitive attribute labels.
